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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Engineering
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JoVE Journal Engineering
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

DOI:
10.3791/64500-v

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04:48 min

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November 30, 2022

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, , ,
1College of Medicine,The Catholic University of Korea, 2Department of Ophthalmology,St. Vincent’s Hospital, 3Yeoncheon Public Medical Center, 4Department of Industrial and Data Engineering,Hongik University, 5Department of Ophthalmology,Uijeongbu St. Mary’s Hospital

Chapters

  • 00:04Introduction
  • 00:42Eyeball, Optic Nerve, and Extraocular Muscle Masking on the Orbital CT Scans
  • 01:42Pre-Processing: Window Clipping and Cropping the VOIs
  • 02:16Four Cross-Validations of the Orbital Segmentation Model
  • 02:57Results: Segmentation Results of the Orbital Structures
  • 04:01Conclusion

Summary

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An object segmentation protocol for orbital computed tomography (CT) images is introduced. The methods of labeling the ground truth of orbital structures by using super-resolution, extracting the volume of interest from CT images, and modeling multi-label segmentation using 2D sequential U-Net for orbital CT images are explained for supervised learning.

Tags

Keywords: Deep LearningMedical Image SegmentationOrbital Computed TomographyObject SegmentationEyeballOptic NerveExtraocular MusclesSuperpixelsSmartPencilSmartFillAutoCorrectionSequence U-NetDice ScoreVolume Similarity

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